Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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An Integrated ML Approach for Detection of Spoofing Assaults in IoT-Networks

Authors
S. Pavithraa1, *, V. Khanaa2
1Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India
2Department of Information Technology, Bharath Institute of Higher Education and Research, Chennai, India
*Corresponding author. Email: pavithraa.it@bharathuniv.ac.in
Corresponding Author
S. Pavithraa
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_5How to use a DOI?
Keywords
IoT security; spoofing attacks; machine learning; multi-factor authentication; secure communication; network resilience
Abstract

IoT has revolutionized various sectors by facilitating automation and improving efficiency through the interconnection of billions of devices. However, this rapid expansion has exposed IoT networks to an increasing number of security vulnerabilities, with spoofing attacks being one of the most prominent threats. Spoofing occurs when malicious entities impersonate legitimate devices to gain unauthorized access, posing risks like data breaches and disruption of critical services. This research proposes a novel IoT architecture designed specifically to counter spoofing attacks through advanced techniques such as machine learning, encryption protocols, and multi-factor authentication. The framework aims to offer an adaptable solution that can operate under varying network conditions, balancing security with performance to ensure robustness and scalability in real-world applications. The research further evaluates the proposed system’s performance, highlighting its effectiveness in mitigating spoofing attempts while ensuring seamless communication across IoT devices.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_5How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - S. Pavithraa
AU  - V. Khanaa
PY  - 2026
DA  - 2026/04/24
TI  - An Integrated ML Approach for Detection of Spoofing Assaults in IoT-Networks
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 46
EP  - 58
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_5
DO  - 10.2991/978-94-6239-654-8_5
ID  - Pavithraa2026
ER  -